Development trends in modern common rail fuel injection systems (FIS) show
dramatically increasing capabilities in terms of optimization of the fuel
injection strategy through a constantly increasing number of injection events
per engine cycle as well as through the modulation and shaping of the injection
rate. In order to fully exploit the potential of the abovementioned fuel
injection strategy optimization, numerical simulation can play a fundamental
role by allowing the creation of a kind of a virtual test rig, where the input
is the fuel injection rate and the optimization targets are the combustion
outputs, such as the burn rate, the pollutant emissions, and the combustion
noise (CN). Starting from a previously developed 1D-CFD (Computational Fluid
Dynamics) virtual test rig, obtained coupling a 1.6L, 4-cylinder turbocharged
diesel engine model with a 1D-CFD injector model, this article presents a
methodology for optimizing the fuel injection strategy aiming to minimize
brake-specific fuel consumption (BSFC) and CN without exceeding the
brake-specific NOx (BSNOx) baseline value. The Non-dominated Sorting Genetic
Algorithm (NSGA-III) was used in GT-SUITE environment for Pareto optimization in
the BSFC-CN space, for three different engine operating conditions in the
low-medium speed and low-medium load range. The proposed approach highlighted
that significant improvements in terms of BSFC and CN can be achieved by
adopting digitalized close pilot events with respect to the Design of
Experiments (DoE) analysis previously presented in [1], also highlighting relevant computational time savings
for the optimization process.
